These functions are wrappers for optim, adding
constrained optimization and fixed parameters.
maxBFGS(fn, grad=NULL, hess=NULL, start, fixed=NULL,
control=NULL,
constraints=NULL,
finalHessian=TRUE,
parscale=rep(1, length=length(start)),
... )maxCG(fn, grad=NULL, hess=NULL, start, fixed=NULL,
control=NULL,
constraints=NULL,
finalHessian=TRUE,
parscale=rep(1, length=length(start)), ...)
maxSANN(fn, grad=NULL, hess=NULL, start, fixed=NULL,
control=NULL,
constraints=NULL,
finalHessian=TRUE,
parscale=rep(1, length=length(start)),
... )
maxNM(fn, grad=NULL, hess=NULL, start, fixed=NULL,
control=NULL,
constraints=NULL,
finalHessian=TRUE,
parscale=rep(1, length=length(start)),
...)
object of class "maxim". Data can be extracted through the following functions:
fn value at maximum (the last calculated value
if not converged.)
estimated parameter value.
vector, last calculated gradient value. Should be close to 0 in case of normal convergence.
matrix of gradients at parameter value estimate
evaluated at each observation (only if grad returns a matrix
or grad is not specified and fn returns a vector).
Hessian at the maximum (the last calculated value if not converged).
integer. Success code, 0 is success (see
optim).
a short message, describing the return code.
logical vector, which parameters are optimized over.
Contains only TRUE-s if no parameters are fixed.
number of iterations. Two-element integer vector giving the number of
calls to fn and gr, respectively.
This excludes those calls needed to
compute the Hessian, if requested, and any calls to fn to compute a
finite-difference approximation to the gradient.
character string, type of maximization.
the optimization control parameters in the form of a
MaxControl object.
The following components can only be extracted directly (with \$):
A list, describing the constrained optimization
(NULL if unconstrained). Includes the following components:
type of constrained optimization
number of iterations in the constraints step
value of the barrier function
function to be maximised. Must have the parameter vector as
the first argument. In order to use numeric gradient
and BHHH method, fn must return a vector of
observation-specific likelihood values. Those are summed internally where
necessary. If the parameters are out of range, fn should
return NA. See details for constant parameters.
gradient of fn. Must have the parameter vector as
the first argument. If NULL, numeric
gradient is used (maxNM and maxSANN do not use
gradient).
Gradient may return
a matrix, where columns correspond to the parameters and rows to the
observations (useful for maxBHHH). The columns are summed internally.
Hessian of fn. Not used by any of these methods, included for
compatibility with maxNR.
initial values for the parameters. If start values are named, those names are also carried over to the results.
parameters to be treated as constants at their
start values. If present, it is treated as an index vector of
start parameters.
list of control parameters or a ‘MaxControl’ object. If it is a list, the default values are used for the parameters that are left unspecified by the user. These functions accept the following parameters:
sqrt(.Machine$double.eps), stopping condition. Relative convergence tolerance: the algorithm stops if the relative improvement between iterations is less than ‘reltol’. Note: for compatibility reason ‘tol’ is equivalent to ‘reltol’ for optim-based optimizers.
integer, maximum number of iterations. Default values are 200 for ‘BFGS’, 500 (‘CG’ and ‘NM’), and 10000 (‘SANN’). Note that ‘iteration’ may mean different things for different optimizers.
integer, larger number prints more working information. Default 0, no information.
1, Nelder-Mead simplex method reflection coefficient (see Nelder & Mead, 1965)
0.5, Nelder-Mead contraction coefficient
2, Nelder-Mead expansion coefficient
% SANN
NULL or a function for "SANN" algorithm
to generate a new candidate point;
if NULL, Gaussian Markov kernel is used
(see argument gr of optim).
10, starting temperature
for the “SANN” cooling schedule. See optim.
10, number of function evaluations at each temperature for
the “SANN” optimizer. See optim.
123, integer to seed random numbers to
ensure replicability of “SANN” optimization and preserve
R random numbers. Use
options like sann_randomSeed=Sys.time() or
sann_randomSeed=sample(100,1) if you want stochastic
results.
either NULL for unconstrained optimization
or a list with two components. The components may be either
eqA and eqB for equality-constrained optimization
\(A \theta + B = 0\); or ineqA and
ineqB for inequality constraints \(A \theta + B > 0\). More
than one
row in ineqA and ineqB corresponds to more than
one linear constraint, in that case all these must be zero
(equality) or positive (inequality constraints).
The equality-constrained problem is forwarded
to sumt, the inequality-constrained case to
constrOptim2.
how (and if) to calculate the final Hessian. Either
FALSE (not calculate), TRUE (use analytic/numeric
Hessian) or "bhhh"/"BHHH" for information equality
approach. The latter approach is only suitable for maximizing
log-likelihood function. It requires the gradient/log-likelihood to
be supplied by individual observations, see maxBHHH for
details.
A vector of scaling values for the parameters.
Optimization is performed on 'par/parscale' and these should
be comparable in the sense that a unit change in any element
produces about a unit change in the scaled value. (see
optim)
further arguments for fn and grad.
Ott Toomet, Arne Henningsen
In order to provide a consistent interface, all these functions also
accept arguments that other optimizers use. For instance,
maxNM accepts the ‘grad’ argument despite being a
gradient-less method.
The ‘state’ (or ‘seed’) of R's random number generator
is saved at the beginning of the maxSANN function
and restored at the end of this function
so this function does not affect the generation of random numbers
although the random seed is set to argument random.seed
and the ‘SANN’ algorithm uses random numbers.
Nelder, J. A. & Mead, R. A, Simplex Method for Function Minimization, The Computer Journal, 1965, 7, 308-313
# Maximum Likelihood estimation of Poissonian distribution
n <- rpois(100, 3)
loglik <- function(l) n*log(l) - l - lfactorial(n)
# we use numeric gradient
summary(maxBFGS(loglik, start=1))
# you would probably prefer mean(n) instead of that ;-)
# Note also that maxLik is better suited for Maximum Likelihood
###
### Now an example of constrained optimization
###
f <- function(theta) {
x <- theta[1]
y <- theta[2]
exp(-(x^2 + y^2))
## you may want to use exp(- theta %*% theta) instead
}
## use constraints: x + y >= 1
A <- matrix(c(1, 1), 1, 2)
B <- -1
res <- maxNM(f, start=c(1,1), constraints=list(ineqA=A, ineqB=B),
control=list(printLevel=1))
print(summary(res))
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